1. Introduction

1.1 Background

In 2018, Capital Metropoitan Transportation Authority (CapMetro), a public transportation agency serving Austin, Travis and parts of Williamson Counties, launched the “Cap Remap” by redesigning the bus network of the city as part of its transit development plan, Connections 2025. Cap Remap adjusted the transit network according to internal analysis and community outreach and aims to provide more fewquent, more reliable, and better connected bus system. Specifically, it remapped certain routes, tripled the number of bus routes that operate every 15 minutes, and made sure the frequency meets the need on weekends.

1.2 Use Case

Given the renewed interest in bus transit in US cities, we think there is an opportunity to innovate in the realm of bus planning using modern data science methods. The goal of this article is to present a tool for planners to test how changes of built environment and/or bus route information impact bus ridership in Austin.

We will start with an evaluation of the Cap Remap to further understand the trend, pattern, and charateristics of the ridership in Austin. Then, we will use the Automated Passenger Counter data and other open data to develope a machine learning model, followed with accuracy result and validation across different regions.

2. Exploratory Analysis

2.1 What Data Are We Using?

The data we used come from the Automated Passenger Counter(APC), which counts the number of boarding and alighting on any given bus. The image below illustrate the APC system at work. source

The data structure is………….

The data here used are disaggregated data in May 21-25th and June 11-15th in 2018, which are one week before and after the CapRemap. The disaggregated data consist of passenger and bus information for each route, each shift, and each stop. Basically, the number of passenger boarding, alighting, and other related ridership information are recorded at each stop for each shift of each bus route.

2.2 Annual Citywide Ridership Trend

How did ridership change before and after CapRemap (06/03, 2018)?

Current available data from Capital Metro allows us to observe the trend in ridership change before and after Cap Remap. The first important part of exploratory analysis is to see the city-wide change in ridership brought by CapRemap. With the stop-level data from Janurary 207 to November 2019, the aggregated city-wide ridership change is shown in the chart below.

……………………need re-edit these………………. The x-axis represents month, and y-axis repredents the average daily ridership in the given month. The solid lines in the chart are 2018 riderships. The yellow solid line is ridership from Janurary to May in 2018 (before Cap Remap happened in June 2018) while the blue solid line is ridership from June to December in 2018 (after CapRemap). The dashed line is the ridership in 2019 from Janurary to June after CapRemap happend the year before.

From the trend in 2018, it is clear that ridership fluctuated between months. Cap Remap didn’t bring a rapid increase in ridership after the implementation. On the contrary, the ridership decreased in June and July. In August, the ridership recovers to the previous level before CapRemap, Then in September the ridership almost exploded to 0.1 million. Then it gradually went down in winter but in 2019, the ridership is generally higher than the same month in 2018. This result shows the general positive impact CapRemap brought to ridership change. For the decrease in June and July and following increasing trend, people might need time to adjust to the new bus schedule and get used to it. And after they realize the convinience of the redesign, the ridership increased rapidly. Another explanation is related to university’s opening and closing as the low ridership happened in summer break and high ridership happened in the beginning of the new semester. ……………………………………………………..S

2.3 Ridership Pattern in Subdivisions

After knowing the trend of city-wide ridership change, the next question is how the ridership changed across the city: which area experienced ridership increase and which area exprienced ridership decrease. Neighborhoods in Austin are used here to show the spatial trend here.

As shown in the map, darker blue represents higher ridership increase, darker red presents lower ridership increase or even ridership decrease. As shown in the map, mostly downtown areas experienced ridership increase from June to September while the outskirts of Austin experienced low ridership increase or even ridership decrease.

We then created charts showing the ridership change in each neighborhood in 2018 in June and September. There are 12 neighborhoods experienced ridership decrease from June to September. There are several neighborhoods experienced high ridership increase of more than 10,000 from June to September. Generally, most neighborhoods experienced ridership increase after CapRemap from June to September. Among the 78 neighborhoods in Austin, we identified three neighborhoods that represents different characteristics: neighborhoods with expected ridership increase; neighborhoods with unexpected ridership increase; neighborhoods with unexpected ridership decrease.

UT is the neighborhood with expected ridership increase.The location of UT neighborhood is just above downtown neighborhood. With a lot of university students living around here, the bus network is sensitive to school schedule. There is a relatively clear trend in ridership change according to school seasons.

The second neighborhood Govalle is the neighborhood that experiencnig unexpected ridership increase. After CapRemap, the ridership in Govalle nearly increased by 50% to 75%. As Govalle is closer to the outskirts of Austin, this ridership increase might reflects CapRemap’success in strengthening the east-west connection.

But there are also neighborhoods exepriencing ridership decrease on the east-west direction. Zilker located in the southwest side of Austin’s downtown region. Its ridership experienced a gradually slight decrease after CapRemap.

2.4 Route Analysis

2.4.1 Route Type

What could potentially influence ridership in terms of route information?

There are several route types, each serving different purposes. Our hypothesis is that they will play an important role in determing the ridership.

……………………. need more description on route types talk about the service area as base map ……………………..

2.4.2 Hotlines

What makes a good bus system? What’s so special about the ‘hotlines’?

The following analysis aim to find out what routes are popular, why are they popular, and how they have changed in a micro perspective. Kmeans Cluster Analysis was used to separate the disaggregated data into groups. Kmeans is an unsupervised learning algorithm that automatically group the dataset based on the distribution of each feature. We intend to use this algorithm to see if the resulting grouping identifies the hotlines, i.e. the routes that have higher ridership.

We looked at the Kmeans analysis both before and after the Cap Remap. We get 4 lines labeled as hotlines before the remap, 6 lines labeled as hotlines after the remap. The hotlines before and after the remap are plotted below. Most of the hot routes are north-south direction. There are two new hotlines emerged after the CapRemap, line 10 and line 20, and they are colored in red.

To dive deeper into the characteristics of the hot bus lines, we map out the passenger load for each route at each stop for each direction. We also ploted the passenger load versus stop sequence ID as well as average boarding and alighting at each stop along each route. The purpose of this analysis is to first, find out what is so special about the hotlines, and second, see trends before and after the Cap Remap. Note that the Austin bus system has different patterns for each route, and in order to make sure the plots to make sense, we only selected the most used pattern for each plot. Below we chose two Line 20 (type High Frequency) and Line 801(Metro Rapid) to demonstrate detailed route analysis.

By mapping and plotting the average passenger number on bus as well as the average boarding and alighting at each stop, we can see better how specific location or neighborhood could potentially contribute to the ridership. These regions will be feature engineered in the following analysis. We also noticed that ridership tends to be higher in the middle portion of the trip, this means a lot of the passengers board from early stops to stops near the ends.

In conclusion, hotlines have the following characteristics:

  • In terms of bus route types, Local, MetroRapid, and High Frequency routes have high ridership
  • In terms of geographical distribution:
  • Go through Hubs (UT, DT, Pleasant Valley)
  • Mostly North-South direction (Following the shape / geography of the city)
  • Going across the a large portion of the city
  • In terms of temporal trend, we know that more Shifts were added in the day time and rush hours, which might increase ridership.

3. Modeling

……………need more text in this section……………..

3.1 Strategies

…………5 types of features………. ……….will use what model………..

3.2 Feature Engineering

…………..some more feature exploring……………

………………feature correlation matrix………….

3.3 Results and Validation

3.3.1 Neighborhood Fixed Effect

3.3.2 School District Fixed Effect

4. Next Steps

  • Using ensemble
  • Tune hyperparameters
  • Further feature engineering

5. Appendix

We group the disaggregated data by routes, and calculated the max and mean number of passengers on bus at each stop, the average miles traveled and the average hours spent for each passenger at each stop, as well as the total run length and total run time of the route, which bacame the features we used for KMeanse Analysis.